RIKEN Center for Biosystems Dynamics Research, Suita, Osaka, Japan.
Research Organization for Information Science and Technology, Chuo, Kobe, Japan.
BMC Bioinformatics. 2020 Jan 29;21(1):33. doi: 10.1186/s12859-019-3338-8.
Studies using quantitative experimental methods have shown that intracellular spatial distribution of molecules plays a central role in many cellular systems. Spatially resolved computer simulations can integrate quantitative data from these experiments to construct physically accurate models of the systems. Although computationally expensive, microscopic resolution reaction-diffusion simulators, such as Spatiocyte can directly capture intracellular effects comprising diffusion-limited reactions and volume exclusion from crowded molecules by explicitly representing individual diffusing molecules in space. To alleviate the steep computational cost typically associated with the simulation of large or crowded intracellular compartments, we present a parallelized Spatiocyte method called pSpatiocyte.
The new high-performance method employs unique parallelization schemes on hexagonal close-packed (HCP) lattice to efficiently exploit the resources of common workstations and large distributed memory parallel computers. We introduce a coordinate system for fast accesses to HCP lattice voxels, a parallelized event scheduler, a parallelized Gillespie's direct-method for unimolecular reactions, and a parallelized event for diffusion and bimolecular reaction processes. We verified the correctness of pSpatiocyte reaction and diffusion processes by comparison to theory. To evaluate the performance of pSpatiocyte, we performed a series of parallelized diffusion runs on the RIKEN K computer. In the case of fine lattice discretization with low voxel occupancy, pSpatiocyte exhibited 74% parallel efficiency and achieved a speedup of 7686 times with 663552 cores compared to the runtime with 64 cores. In the weak scaling performance, pSpatiocyte obtained efficiencies of at least 60% with up to 663552 cores. When executing the Michaelis-Menten benchmark model on an eight-core workstation, pSpatiocyte required 45- and 55-fold shorter runtimes than Smoldyn and the parallel version of ReaDDy, respectively. As a high-performance application example, we study the dual phosphorylation-dephosphorylation cycle of the MAPK system, a typical reaction network motif in cell signaling pathways.
pSpatiocyte demonstrates good accuracies, fast runtimes and a significant performance advantage over well-known microscopic particle methods in large-scale simulations of intracellular reaction-diffusion systems. The source code of pSpatiocyte is available at https://spatiocyte.org.
使用定量实验方法的研究表明，分子的细胞内空间分布在许多细胞系统中起着核心作用。空间分辨计算机模拟可以整合这些实验的定量数据，构建系统的物理精确模型。尽管计算成本很高，但微观分辨率反应-扩散模拟器（如 Spatiocyte）可以通过在空间中显式表示单个扩散分子，直接捕获由扩散限制反应和拥挤分子的体积排除组成的细胞内效应。为了减轻通常与模拟大或拥挤的细胞内隔室相关的陡峭计算成本，我们提出了一种称为 pSpatiocyte 的并行化 Spatiocyte 方法。
新的高性能方法在六方密堆积（HCP）晶格上采用独特的并行化方案，有效地利用了普通工作站和大型分布式内存并行计算机的资源。我们引入了一种用于快速访问 HCP 晶格体素的坐标系、并行化事件调度程序、用于单分子反应的并行化 Gillespie 直接法，以及用于扩散和双分子反应过程的并行化事件。我们通过与理论比较验证了 pSpatiocyte 反应和扩散过程的正确性。为了评估 pSpatiocyte 的性能，我们在 RIKEN K 计算机上进行了一系列并行化扩散运行。在晶格细离散化且体素占有率低的情况下，pSpatiocyte 表现出 74%的并行效率，与 64 核运行时间相比，实现了 7686 倍的加速。在弱扩展性能方面，pSpatiocyte 至少达到 60%的效率，最大可使用 663552 核。在八核工作站上执行 Michaelis-Menten 基准模型时，pSpatiocyte 的运行时间分别比 Smoldyn 和 ReaDDy 的并行版本短 45 倍和 55 倍。作为高性能应用示例，我们研究了 MAPK 系统的双磷酸化-去磷酸化循环，这是细胞信号通路中典型的反应网络基元。
pSpatiocyte 在大规模细胞内反应-扩散系统模拟中具有良好的准确性、快速的运行时间和比知名微观粒子方法显著的性能优势。pSpatiocyte 的源代码可在 https://spatiocyte.org 获得。
